{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:50:10Z","timestamp":1774450210439,"version":"3.50.1"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T00:00:00Z","timestamp":1633910400000},"content-version":"vor","delay-in-days":283,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006261","name":"Taif University","doi-asserted-by":"publisher","award":["TURDP-2020\/253"],"award-info":[{"award-number":["TURDP-2020\/253"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The generalized log\u2010logistic distribution is especially useful for modelling survival data with variable hazard rate shapes because it extends the log\u2010logistic distribution by adding an extra parameter to the classical distribution, resulting in greater flexibility in analyzing and modelling various data types. We derive the fundamental mathematical and statistical properties of the proposed distribution in this paper. Many well\u2010known lifetime special submodels are included in the proposed distribution, including the Weibull, log\u2010logistic, exponential, and Burr XII distributions. The maximum likelihood method was used to estimate the unknown parameters of the proposed distribution, and a Monte Carlo simulation study was run to assess the estimators\u2019 performance. This distribution is significant because it can model both monotone and nonmonotone hazard rate functions, which are quite common in survival and reliability data analysis. Furthermore, the proposed distribution\u2019s flexibility and usefulness are demonstrated in a real\u2010world data set and compared to its submodels, the Weibull, log\u2010logistic, and Burr XII distributions, as well as other three\u2010parameter parametric survival distributions, such as the exponentiated Weibull distribution, the three\u2010parameter log\u2010normal distribution, the three\u2010parameter (or the shifted) log\u2010logistic distribution, the three\u2010parameter gamma distribution, and an exponentiated Weibull distribution. The proposed distribution is plausible, according to the goodness\u2010of\u2010fit, log\u2010likelihood, and information criterion values. Finally, for the data set, Bayesian inference and Gibb\u2019s sampling performance are used to compute the approximate Bayes estimates as well as the highest posterior density credible intervals, and the convergence diagnostic techniques based on Markov chain Monte Carlo techniques were used.<\/jats:p>","DOI":"10.1155\/2021\/5820435","type":"journal-article","created":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T03:06:38Z","timestamp":1634007998000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Bayesian and Classical Inference for the Generalized Log\u2010Logistic Distribution with Applications to Survival Data"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4139-7334","authenticated-orcid":false,"given":"Abdisalam Hassan","family":"Muse","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9703-6514","authenticated-orcid":false,"given":"Samuel","family":"Mwalili","sequence":"additional","affiliation":[]},{"given":"Oscar","family":"Ngesa","sequence":"additional","affiliation":[]},{"given":"Saad J.","family":"Almalki","sequence":"additional","affiliation":[]},{"given":"Gamal A.","family":"Abd-Elmougod","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,10,11]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1002\/(sici)1521-4036(199907)41:4<431::aid-bimj431>3.0.co;2-u"},{"key":"e_1_2_13_2_2","doi-asserted-by":"publisher","DOI":"10.2991\/jsta.2014.13.1.6"},{"key":"e_1_2_13_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.4710300714"},{"key":"e_1_2_13_4_2","doi-asserted-by":"publisher","DOI":"10.2307\/2347295"},{"key":"e_1_2_13_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/60.2.267"},{"key":"e_1_2_13_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40488-016-0054-z"},{"key":"e_1_2_13_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/math9121386"},{"key":"e_1_2_13_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/2193787"},{"key":"e_1_2_13_9_2","doi-asserted-by":"publisher","DOI":"10.18187\/pjsor.v16i4.2961"},{"key":"e_1_2_13_10_2","doi-asserted-by":"publisher","DOI":"10.11648\/j.ijsd.20190502.12"},{"key":"e_1_2_13_11_2","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2013.775307"},{"key":"e_1_2_13_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2017.1388399"},{"key":"e_1_2_13_13_2","doi-asserted-by":"publisher","DOI":"10.1590\/0001-3765201720150579"},{"key":"e_1_2_13_14_2","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2014.909937"},{"key":"e_1_2_13_15_2","doi-asserted-by":"publisher","DOI":"10.17713\/ajs.v45i3.107"},{"key":"e_1_2_13_16_2","doi-asserted-by":"publisher","DOI":"10.1214\/12-bjps209"},{"key":"e_1_2_13_17_2","doi-asserted-by":"publisher","DOI":"10.12785\/jsap\/020102"},{"key":"e_1_2_13_18_2","doi-asserted-by":"publisher","DOI":"10.12988\/ams.2013.35268"},{"key":"e_1_2_13_19_2","doi-asserted-by":"publisher","DOI":"10.2991\/jsta.2013.12.3.2"},{"key":"e_1_2_13_20_2","first-page":"2250","article-title":"Exponential\u2013log logistic additive failure rate model","volume":"4","author":"Rosaiah K.","year":"2014","journal-title":"International Journal of Scientific and Research Publications,"},{"key":"e_1_2_13_21_2","doi-asserted-by":"publisher","DOI":"10.5539\/ijsp.v10n3p93"},{"key":"e_1_2_13_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm11010013"},{"key":"e_1_2_13_23_2","unstructured":"YahayaA.andDewuM. 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